US11335089B2 - Food detection and identification method based on deep learning - Google Patents
Food detection and identification method based on deep learning Download PDFInfo
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- US11335089B2 US11335089B2 US16/892,330 US202016892330A US11335089B2 US 11335089 B2 US11335089 B2 US 11335089B2 US 202016892330 A US202016892330 A US 202016892330A US 11335089 B2 US11335089 B2 US 11335089B2
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- G06N3/02—Neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/68—Food, e.g. fruit or vegetables
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- the present invention relates to the field of computer vision, and particularly relates to a food detection and identification method based on deep learning.
- an automatic billing system of a dining hall mainly has a traditional identification and billing solution based on RFID tags and an automatic food identification solution based on deep learning of a convolutional neural network.
- the former solution has the advantages that an identification accuracy rate is high, the same set of RFID tags can be suitable for all restaurants and practical promotion is convenient, and has the disadvantages of high cost and easy consumption of RFID chip, under a high temperature environment.
- the latter solution has the advantages of needing no reformation of the existing dinner plates and dinnerware, directly identifying food names and facilitating background analysis of subsequent sales volume and health data, and has the disadvantages that a large number of food pictures of a restaurant shall be collected for each restaurant, and all food in the pictures shall be manually labeled on the collected pictures to form labeled data for deep learning and model learning, which consumes time and money and greatly influences the large-scale promotion based on a deep learning, solution.
- the patent of the present invention proposes an improved food detection and identification method based on deep learning.
- the core is to train a general multi-target positioning network applicable to all restaurants and a classification network for each restaurant through a deep convolutional neural network.
- the general multi-target positioning network only needs to be trained once. After the training is completed, the food in different restaurants can be positioned.
- the classification network can be used to identify after positioning.
- the classification network does not need expensive and time-consuming manual labeling of the collected pictures.
- the food pictures of the restaurants only need to be placed in corresponding directories according to the food category to start the training process. Therefore, the combination method of the general multi-target positioning network and the classification network proposed by the present invention can greatly reduce the labor cost and can be promoted and applied in large scale.
- the purpose of the present invention is to position and identify the food through a general multi-target positioning network and a classification network, to detect the food and adapt to various actual, restaurant environments.
- the present invention provides a food detection and identification method based on deep learning, comprising:
- the step of building the food sample library S p according to the obtained food pictures and enhancing data comprises:
- the step of rebuilding the sample library S p into a classification sample library S c by category comprises:
- the step of training the rebuilt classification food sample library S c through the convolutional neural network to obtain the classification network Net c comprises:
- the step of enabling the food pictures to be detected to sequentially pass through the general multi-target positioning network Net p and the classification network Nets to obtain an identification result comprises:
- the food detection and identification method based on deep learning in the present invention identifies the food through two convolutional neural networks with different functions, i.e., the general multi-target positioning network and the classification network.
- the present invention firstly establishes the food sample library S p through actual collection, trains the multi-target positioning network through the deep convolutional neural network after labeling to determine the position of each food in the food pictures to be detected, then rebuilds the labeled food sample library S p into a food classification sample library S c by category, and then trains the classification network through the deep convolutional neural network to determine the food category in the food pictures to be detected, so as to obtain a final identification result.
- the present invention conducts identification by two deep convolutional neural networks with different functions. Compared with the existing method which generally uses one general convolutional neural network, the present invention reduces the collection and labeling cost of the food.
- the general multi-target positioning network can be used generally in different restaurants without re-collecting food samples. Secondly, for special food of different restaurants, the general multi-target positioning network can also be used to rebuild the classification sample library, thereby reducing labor cost, shortening a training cycle and facilitating subsequent popularization.
- FIG. 1 is a flow chart of a food detection and identification method based on deep learning in an embodiment of the present application.
- FIG. 2 is a flow chart of building of a food sample library S p and a classification sample library S c and training of a general multi-target positioning network Net p and a classification network Net c in an embodiment of the present application.
- FIG. 3 is a flow chart of a food identification process in an embodiment of the present application.
- FIG. 4 is a closed-loop flow chart of a general multi-target positioning network Net p and a classification network Net c in an embodiment of the present application.
- FIG. 1 shows an entire flow chart of an embodiment of the present application. Specific implementation is as follows:
- step S 110 collecting food sample pictures in an actual restaurant and labeling; building a food sample library S p ; training a general multi-target positioning network Net p by using a deep convolutional neural network; rebuilding a classification sample library S p , according to the food sample library S p ; and training a classification network Net through deep convolutional neural network;
- step S 120 firstly obtaining positioning information of the food for the food pictures to be detected through the general multi-target positioning network Net p ;
- step S 130 extracting each food picture from the food pictures to be detected through the position information
- step S 140 obtaining each food category for the extracted food pictures through the classification network Net c ;
- step S 150 obtaining a final identification result of the food pictures to be detected.
- FIG. 2 shows the building of a food sample data set and a classification data set, and training of a general multi-target positioning network Net p and a classification network Net c , wherein
- the initial food sample data set includes food with large color and shape spans.
- Step S 210 labeling the collected food picture data, and building a food sample library S p ;
- Step S 220 training by using the food sample library S p through a deep convolutional neural network to obtain a general multi-target positioning network Net p ;
- Step S 230 rebuilding the food sample library S p into a food classification sample library S c (in practical application, the general multi-target positioning network Net p can be used to create special food categories, and the food classification sample library S c of each restaurant can also be used to update the food sample library S p );
- Step S 240 training a classification network Net c by using the rebuilt classification sample library S c .
- FIG. 3 shows a food identification process, including:
- step S 310 obtaining positioning information of the food for the food pictures to be detected through the general multi-target positioning network Net p ;
- step S 320 extracting each food picture from the food pictures to be detected according to the position information
- step S 330 obtaining each food category for the extracted food pictures through the classification network Net c ;
- step S 340 obtaining a final identification result.
- FIG. 4 shows that the general multi-target positioning network Net p and the classification network Net c mutually update the sample libraries S p and S c , including:
- step S 410 rebuilding the food sample library S p into a food classification sample library S c ;
- step S 420 using the general multi-target positioning network Net p to update the food classification sample library S c from different restaurant food categories, thereby improving the accuracy of the classification network Net c ;
- step S 430 using the updated food classification sample library S c to update the food sample library S p through data enhancement, thereby improving the accuracy of the general multi-target positioning network Net p .
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CN201910586649.9A CN110399804A (en) | 2019-07-01 | 2019-07-01 | A kind of food inspection recognition methods based on deep learning |
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CN113077420B (en) * | 2021-03-19 | 2024-05-31 | 江南大学 | Fish fiber evaluation method and system based on convolutional neural network |
CN113033706B (en) * | 2021-04-23 | 2022-04-29 | 广西师范大学 | Multi-source two-stage dish identification method based on visual detection and re-identification |
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